Multi-Domain Sentiment Analysis |
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| © 2021 by IJCTT Journal | ||
| Volume-69 Issue-3 |
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| Year of Publication : 2021 | ||
| Authors : Sonali Aggarwal | ||
| DOI : 10.14445/22312803/IJCTT-V69I3P115 | ||
How to Cite?
Sonali Aggarwal, "Multi-Domain Sentiment Analysis," International Journal of Computer Trends and Technology, vol. 69, no. 3, pp. 85-89, 2021. Crossref, 10.14445/22312803/IJCTT-V69I4P115
Abstract
I considered the problem of classifying amazon reviews by overall sentiment, i.e., positive or negative. Given a review that could be positive ( 4 or 5 stars) or negative (1 or 2 stars), the task is to accurately perform the binary classification. The review can be in any domain like books, electronics, DVDs, Kitchen, and others. However, in this project, I have limited myself to books, DVDs, and Kitchen. I investigated the performance of supervised machine learning methods like Naive Bayes, Support Vector machines(SVM), and Decision Trees for the problem of classification based on the overall sentiment of the reviewer. I also tested how well a classifier trained in one domain performs on the other domains.
Keywords
Amazon Reviews, Sentiment Analysis, Supervised Machine Learning.
Reference
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